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Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels [chapter]

Erik Rodner, Alexander Freytag, Paul Bodesheim, Joachim Denzler
2012 Lecture Notes in Computer Science  
We present how to perform exact large-scale multi-class Gaussian process classification with parameterized histogram intersection kernels.  ...  To handle the additional model flexibility induced by parameterized kernels, our approach is able to optimize the parameters with large-scale training data.  ...  Denzler: Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels. ECCV 2012, pp. 85-98. (c) Copyright by Springer.  ... 
doi:10.1007/978-3-642-33765-9_7 fatcat:neewf2dbrfdnlkdxlm7izqlebu

An Image Classification Algorithm Based on Bag of Visual Words and Multi-kernel Learning

Xiong-wei LOU, De-cai HUANG, Lu-ming FAN, Ai-jun XU
2014 Journal of Multimedia  
At last, the final classification results are given by generalized multiple kernel proposed by this paper.  ...  First of all, we extract the D-SIFT (Dense Scale-invariant Feature Transform) features from images in the training set. And then construct visual vocabulary via K-means clustering.  ...  Next, process the spatial pyramid histograms previously generated via generalized Gaussian Combinatory Kernel function.  ... 
doi:10.4304/jmm.9.2.269-277 fatcat:mjiq2xpfzfdlfbymrcfkf65jv4

HEp-2 Cell Classification: The Role of Gaussian Scale Space Theory as A Pre-processing Approach [article]

Xianbiao Qi, Guoying Zhao, Jie Chen, Matti Pietikäinen
2015 arXiv   pre-print
In this paper, we analyze the importance of the pre-processing, and investigate the role of Gaussian Scale Space (GSS) theory as a pre-processing approach for the HEp-2 cell classification task.  ...  Under the BoW framework, the introduced pre-processing approach, using only one Local Orientation Adaptive Descriptor (LOAD), achieved superior performance on the Executable Thematic on Pattern Recognition  ...  Acknowledgements The authors would like to thank the organizers of the HEp-2 cell classification contests. This work was supported by the Academy of Finland and Infotech Oulu.  ... 
arXiv:1509.02320v1 fatcat:sgpjornghrbwbj3xiwv3bg3pey

Multi-Scale Spatially Weighted Local Histograms in O(1) [article]

Mahdieh Poostchi, Ali Shafiekhani, Kannappan Palaniappan, Guna Seetharaman
2017 arXiv   pre-print
This paper presents a novel algorithm to compute accurately multi-scale Spatially weighted local histograms in constant time using Weighted Integral Histogram (SWIH) for fast search.  ...  We applied our spatially weighted integral histogram approach for fast tracking and obtained more accurate and robust target localization result in comparison with using plain histogram.  ...  small scale to very large scale.  ... 
arXiv:1705.03524v1 fatcat:tzxlgfvqcrh5npnnnti5k4i6cu

Applications of Locally Orderless Images [chapter]

Bram van Ginneken, Bart M. ter Haar Romeny
1999 Lecture Notes in Computer Science  
The LOI represents the image, observed at inner scale σ , as a local histogram with bin-width β, at each location, with a Gaussian-shape region of interest of extent α.  ...  We present applications for a range of image processing tasks, including new nonlinear diffusion schemes, adaptive histogram equalization and variations, several methods for noise and scratch removal,  ...  ACKNOWLEDGMENTS This work is supported by the IOP Image Processing funded by the Dutch Ministry of Economic Affairs.  ... 
doi:10.1007/3-540-48236-9_2 fatcat:avj5x335zzbltozlrflal5fqwu

Applications of Locally Orderless Images

Bram van Ginneken, Bart M. ter Haar Romeny
2000 Journal of Visual Communication and Image Representation  
The LOI represents the image, observed at inner scale σ , as a local histogram with bin-width β, at each location, with a Gaussian-shape region of interest of extent α.  ...  We present applications for a range of image processing tasks, including new nonlinear diffusion schemes, adaptive histogram equalization and variations, several methods for noise and scratch removal,  ...  ACKNOWLEDGMENTS This work is supported by the IOP Image Processing funded by the Dutch Ministry of Economic Affairs.  ... 
doi:10.1006/jvci.1999.0445 fatcat:dhlw5kop35hz7mtmjn2kmhjmj4

An adaptive skin model and its application to objectionable image filtering

Qiang Zhu, Ching-Tung Wu, Kwang-Ting Cheng, Yi-Leh Wu
2004 Proceedings of the 12th annual ACM international conference on Multimedia - MULTIMEDIA '04  
We propose an adaptive skin-detection method, which allows modelling and detection of the true skin-color pixels with significantly higher accuracy and flexibility than previous methods.  ...  Results of extensive experiments on large databases demonstrate the effectiveness and benefits of our adaptive skin-model.  ...  In the training process, some common techniques, such as scaling feature values and cross-validation based model selection, were applied.  ... 
doi:10.1145/1027527.1027538 dblp:conf/mm/ZhuWCW04 fatcat:hf6tgql5irdf3efdwf7xyese2e

3D Content-Based Retrieval in Artwork Databases

David Gorisse, Matthieu Cord, Michel Jordan, Sylvie Philipp-Foliguet, Frederic Precioso
2007 2007 3DTV Conference  
Some 3D descriptors are used, in association with our active learning search engine RETIN. 3D features are described as well as our new system of classification and retrieval of objects, which we called  ...  In this paper, we present first results obtained in the frame of the EROS-3D project, which aims at dealing with a collection of artwork 3D models, i.e. visualize them, classify them and compare them.  ...  We have compared various kernel functions, and then adopted a Gaussian kernel, with various distances.  ... 
doi:10.1109/3dtv.2007.4379434 fatcat:su3veyydgfd2jk4bj7naofmaze

Review on Kernel based Target Tracking for Autonomous Driving

Yanming Wang, Jiguang Yue, Yanchao Dong, Zhencheng Hu
2016 Journal of Information Processing  
The theoretical and experimental analysis allow us to conclude that the kernel based online subspace learning algorithm achieves a good trade-off between the stability and real-time processing for target  ...  This paper reviews the kernel theory adopted in target tracking of autonomous driving and makes a qualitative and quantitative comparison among several well-known kernel based methods.  ...  Collins [62] employed spatial kernel and scale kernel deal with c 2016 Information Processing Society of Japan [63] adopted a dual-kernel for visual tracking, one for the similarities between candidates  ... 
doi:10.2197/ipsjjip.24.49 fatcat:yvyqgiug6nclzdn5jqailxmm7i

Sampling Strategies for Bag-of-Features Image Classification [chapter]

Eric Nowak, Frédéric Jurie, Bill Triggs
2006 Lecture Notes in Computer Science  
We also study the influence of other factors including codebook size and creation method, histogram normalization method and minimum scale for feature extraction.  ...  Bag-of-features representations have recently become popular for content based image classification owing to their simplicity and good performance.  ...  Fig. 1 . 1 Examples of multi-scale sampling methods. (1) Harris-Laplace (HL) with a large detection threshold. (2) HL with threshold zero -note that the sampling is still quite sparse. (3) Laplacian-of-Gaussian  ... 
doi:10.1007/11744085_38 fatcat:x3f2fhsrpbdufab33qaxcpsl3m

Review of Local Descriptor in RGB-D Object Recognition

Ema Rachmawati, Iping Supriana Suwardi, Masayu Leylia Khodra
2014 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
We also highlight the involvement of depth images and how they can be combined with RGB images in constructing a local descriptor.  ...  Three different approaches are used in involving depth images into compact feature representation, that is classical approach using distribution based, kernel-trick, and feature learning.  ...  the Gaussian kernel.  ... 
doi:10.12928/telkomnika.v12i4.388 fatcat:4rdsur3agfbg3b57boz3xoasxq

An Adaptive Face Recognition System Based on a Novel Incremental Kernel Nonparametric Discriminant Analysis

2019 KSII Transactions on Internet and Information Systems  
This paper introduces an adaptive face recognition method based on a Novel Incremental Kernel Nonparametric Discriminant Analysis (IKNDA) that is able to learn through time.  ...  Thus, it handles dynamic and large data in a better way.  ...  For data kernelization, we utilize the Gaussian RBF kernel ( , ) = −‖ − ‖ 2 ⁄ . In fact, it was shown to be flexible and robust [24] . Here σ denotes positive "width" parameter.  ... 
doi:10.3837/tiis.2019.04.022 fatcat:zdmcgbk3rbajpaaddddcbtatli

A Review of Codebook Models in Patch-Based Visual Object Recognition

Amirthalingam Ramanan, Mahesan Niranjan
2011 Journal of Signal Processing Systems  
However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties.  ...  The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied.  ...  The classification results were obtained by one-versus-all SVMs with Gaussian kernel.  ... 
doi:10.1007/s11265-011-0622-x fatcat:bowdhn7xrvbxfbt5k2ernaud3m

Hierarchical Gaussianization for image classification

Xi Zhou, Na Cui, Zhen Li, Feng Liang, Thomas S Huang
2009 2009 IEEE 12th International Conference on Computer Vision  
We compare our new representation with other approaches in scene classification, object recognition and face recognition, and our performance ranks among the top in all three tasks.  ...  We justify that the traditional histogram representation and the spatial pyramid matching are special cases of our hierarchical Gaussianization.  ...  As a flexible way to model a variety of distributions, GMM emerged as a better alternative to histograms in age estimation, object classification and video event analysis [2, 1, 3] .  ... 
doi:10.1109/iccv.2009.5459435 dblp:conf/iccv/ZhouCLLH09 fatcat:oqf2dg3s2vakfcrmpkyrlsmylq

Struck: Structured Output Tracking with Kernels

Sam Hare, Stuart Golodetz, Amir Saffari, Vibhav Vineet, Ming-Ming Cheng, Stephen L. Hicks, Philip H.S. Torr
2016 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Our method uses a kernelized structured output support vector machine (SVM), which is learned online to provide adaptive tracking.  ...  Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model.  ...  Combining kernels. A: Haar features with Gaussian kernel (σ = 0.2); B: Raw features with Gaussian kernel (σ = 0.1); C: Histogram features with intersection kernel.  ... 
doi:10.1109/tpami.2015.2509974 pmid:26700968 fatcat:cepbz2h3rjeqvh3wgrzdaqfqpe
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